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This paper focuses on designing robust symbol-level precoding (SLP) in anoverlay cognitive radio (CR) network, where the primary and secondary networkstransmit signals concurrently. When the primary base station (PBS) shares dataand perfect channel state information (CSI) with the cognitive base station(CBS), we derive an SLP approach that minimizes the CR transmission power andsatisfies symbol-wise Safety Margin (SM) constraints of both primary users(PUs) and cognitive users (CUs). The resulting optimization has a quadraticobjective and linear inequality (LI) constraints, which can be solved bystandard convex methods. For the case of imperfect CSI from the PBS, we proposerobust SLP schemes. First, with a norm-bounded CSI error model to approximatethe uncertain channels, we adopt a max-min philosophy to conservatively achieverobust SLP constraints. Second, we use the additive quantization noise model(AQNM) to describe the quantized PBS CSI and employ a stochastic constraint toformulate the problem. Both robust approaches also result in a quadraticobjective with LI constraints. Simulation results show that, rather than simplytrying to eliminate the network's cross-interference, the proposed robust SLPschemes enable the primary and secondary networks to aid each other in meetingtheir quality of service constraints.

Robust Symbol Level Precoding for Overlay Cognitive Radio Networks